• A controlled migration genetic algorithm operator for hardware-in-the-loop experimentation

      Gladwin, Daniel; Stewart, Paul; Stewart, Jill; University of Sheffield (International Federation of Automatic Control, 2011-06)
      In this paper, we describe the development of an extended migration operator, which combats the negative effects of noise on the effective search capabilities of genetic algorithms. The research is motivated by the need to minimize the num-ber of evaluations during hardware-in-the-loop experimentation, which can carry a significant cost penalty in terms of time or financial expense. The authors build on previous research, where convergence for search methods such as Simulated Annealing and Variable Neighbourhood search was accelerated by the implementation of an adaptive decision support operator. This methodology was found to be effective in searching noisy data surfaces. Providing that noise is not too significant, Genetic Al-gorithms can prove even more effective guiding experimentation. It will be shown that with the introduction of a Controlled Migration operator into the GA heuristic, data, which repre-sents a significant signal-to-noise ratio, can be searched with significant beneficial effects on the efficiency of hardware-in-the-loop experimentation, without a priori parameter tuning. The method is tested on an engine-in-the-loop experimental example, and shown to bring significant performance benefits.
    • Internal combustion engine control for series hybrid electric vehicles by parallel and distributed genetic programming/multiobjective genetic algorithms

      Gladwin, Daniel; Stewart, Paul; Stewart, Jill; University of Sheffield; University of Salford (Taylor & Francis, 2010-03-01)
      This article addresses the problem of maintaining a stable rectified DC output from the three-phase AC generator in a series-hybrid vehicle powertrain. The series-hybrid prime power source generally comprises an internal combustion (IC) engine driving a three-phase permanent magnet generator whose output is rectified to DC. A recent development has been to control the engine/generator combination by an electronically actuated throttle. This system can be represented as a nonlinear system with significant time delay. Previously, voltage control of the generator output has been achieved by model predictive methods such as the Smith Predictor. These methods rely on the incorporation of an accurate system model and time delay into the control algorithm, with a consequent increase in computational complexity in the real-time controller, and as a necessity relies to some extent on the accuracy of the models. Two complementary performance objectives exist for the control system. Firstly, to maintain the IC engine at its optimal operating point, and secondly, to supply a stable DC supply to the traction drive inverters. Achievement of these goals minimises the transient energy storage requirements at the DC link, with a consequent reduction in both weight and cost. These objectives imply constant velocity operation of the IC engine under external load disturbances and changes in both operating conditions and vehicle speed set-points. In order to achieve these objectives, and reduce the complexity of implementation, in this article a controller is designed by the use of Genetic Programming methods in the Simulink modelling environment, with the aim of obtaining a relatively simple controller for the time-delay system which does not rely on the implementation of real time system models or time delay approximations in the controller. A methodology is presented to utilise the miriad of existing control blocks in the Simulink libraries to automatically evolve optimal control structures.
    • A novel genetic programming approach to the design of engine control systems for the voltage stabilization of hybrid electric vehicle generator outputs

      Gladwin, Daniel; Stewart, Paul; Stewart, Jill; University of Sheffield; University of Lincoln (Institution of Mechanical Engineers, 2011-07-13)
      This paper describes a Genetic Programming based automatic design methodology applied to the maintenance of a stable generated electrical output from a series-hybrid vehicle generator set. The generator set comprises a three-phase AC generator whose output is subsequently rectified to DC. The engine/generator combination receives its control input via an electronically actuated throttle, whose control integration is made more complex due to the significant system time delay. This time delay problem is usually addressed by model predictive design methods, which add computational complexity and rely as a necessity on accurate system and delay models. In order to eliminate this reliance, and achieve stable operation with disturbance rejection, a controller is designed via a Genetic Programming framework implemented directly in Matlab and, particularly, Simulink. The principal objective is to obtain a relatively simple controller for the time-delay system which does not rely on computationally expensive structures, yet retains inherent disturbance rejection properties. A methodology is presented to automatically design control systems directly upon the block libraries available in Simulink to automatically evolve robust control structures.